Distributed solar installations have become increasingly common in Palestinian distribution networks, creating an operational paradox: while these systems reduce import dependence, they often generate more power than can be consumed locally during peak hours. This surplus energy flows backward through the grid—a phenomenon that complicates network management and can trigger unfavorable net metering arrangements. Researchers addressing this challenge developed a practical solution: intelligent coordination between EV charging schedules and real-time solar generation patterns.
The study deployed actual operational data from a Siemens PAC3200T power quality meter installed at the connection point of the Far'ata–Immatain distribution feeder. Rather than relying on complex predictive models or requiring extensive network data, the team created a rule-based framework that responds dynamically to measured PV output and network conditions. When solar generation exceeds local demand, the system triggers EV charging sessions, converting otherwise-wasted renewable energy into grid support through demand-side management.
Simulation results across four scenarios with increasing EV penetration showed consistent improvements. At the highest penetration level tested, the framework absorbed 200 kW of surplus PV generation that would otherwise have reversed onto the feeder. Critically, reverse power exports were eliminated entirely under normal operating conditions, addressing the utility's core concern about unfavorable net metering settlements.
This approach offers significant advantages for resource-constrained utilities. It requires minimal infrastructure investment, operates with limited data, and scales naturally as EV adoption increases. The measurement-driven design means utilities can implement it using existing power quality monitoring equipment without extensive grid modeling or costly reinforcement projects. For Palestinian distribution operators and utilities globally facing similar solar integration challenges, this framework demonstrates a pragmatic path forward: leveraging the growing EV fleet as a flexible load resource rather than viewing it as an additional demand burden.
